AI Development
Production ML APIs & Generative Pipelines
We build and integrate production-grade machine learning pipelines, fine-tuned models, and custom generative AI backends into real web and mobile applications.
Common Challenges We Solve
High Inference Costs & Latency
Inefficient foundation model calls driving up monthly token bills and slowing down client responses.
Model Hallucinations in Production
Generative models returning inaccurate outputs without validation guards or structured outputs.
Complex Multi-Model Integration
Struggling to connect proprietary domain data with modern embeddings, vector databases, and APIs.
Lack of Production Monitoring
Deploying AI models without token telemetry, latency tracking, eval datasets, or drift detection.
What We Deliver
Fine-Tuned Models & SLMs
Adapting open weights (Llama, Mistral, Gemma) or proprietary models on your specialized domain datasets.
ML API Backend Engineering
High-throughput asynchronous FastAPI and Python endpoints with structured JSON outputs and schema validation.
Embedding & Vector Search
Vector database setup, hybrid search pipelines, and semantic similarity search using pgvector, Qdrant, or Pinecone.
Evaluation & Guardrails
Automated regression testing, prompt eval harnesses, schema validation, and guardrails to prevent harmful or inaccurate responses.
How We Deliver AI Development
Dataset & Feasibility Audit
Evaluate your data assets, latency constraints, and cost envelope before writing pipeline code.
Model Selection & Prototype
Benchmark proprietary vs. self-hosted open-weights options against a fixed evaluation set.
Hardened Production API
Implement streaming endpoints, caching, rate limiting, and robust error fallback mechanisms.
Monitoring & Continuous Evals
Set up telemetry for cost per request, p95 latency, and output quality metrics.
Core Stack for AI Development
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